Freelance Researcher – AI & Machine Learning (Adversarial Financial Fraud Detection)

Workfoster

Website: workfoster.com
Job details:

Job Title: Freelance Researcher – AI & Machine Learning (Adversarial Financial Fraud Detection)

Job Type: Freelancer

Experience Level: Mid–Senior (2–4+ Years)

Domain: AI, Machine Learning, Financial Fraud Detection, Adversarial ML

Role Overview:

We are seeking a highly skilled Freelance AI/ML Researcher to lead advanced research in adversarial financial fraud detection. The role involves designing, replicating, and extending state-of-the-art Transformer-based fraud detection models under data poisoning and adversarial attack scenarios, with a strong focus on robustness, reinforcement learning–based defenses, and reproducible research outcomes.

This engagement is research-intensive and aimed at delivering Ph.D.-level outputs, including publication-ready results and a Scopus-aligned dissertation or research paper.

Key Responsibilities:

• Lead end-to-end research, design, and implementation of AI/ML-based financial fraud detection systems under adversarial data poisoning conditions.
• Replicate, benchmark, and extend state-of-the-art Transformer-based fraud detection models using real-world financial transaction datasets.
• Design and simulate controlled data poisoning attacks, including:

Label flipping

Feature manipulation

Subpopulation poisoning

Backdoor attacks

• Develop a robust Transformer-based fraud detection framework resistant to adversarial manipulation.
• Integrate a Reinforcement Learning (RL)–based adaptive defense layer to counter evolving threats, concept drift, and poisoning patterns.
• Build an ensemble defense architecture combining:

Transformer predictions

RL agent decisions

Confidence scores from state-of-the-art models

• Conduct comprehensive experimental evaluations under clean and poisoned data conditions using standard fraud detection metrics.
• Ensure full reproducibility by delivering clean code, experiment logs, datasets, and documentation.
• Prepare publication-ready research outputs, including implementation details, evaluations, and technical write-ups.

Required Qualifications:

• Master’s or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, Data Science, Cybersecurity, or a related field.
• Strong academic or applied research background in fraud detection, adversarial machine learning, or cybersecurity analytics.
• Demonstrated expertise in deep learning architectures, particularly Transformers, and reinforcement learning systems.

Required Experience:

• 2–4+ years of hands-on experience in applied machine learning, fraud detection, or adversarial ML research.
• Prior experience working with financial transaction datasets or tabular ML problems.
• Proven ability to replicate published research models and conduct comparative experimental evaluations.

Technical Skills & Tools/:

• Programming: Python
• ML / DL Frameworks: TensorFlow, PyTorch
• Data Processing: NumPy, Pandas, Scikit-learn, large-scale CSV datasets
• Visualization & Analysis: Matplotlib, Seaborn
• Experimentation & Reproducibility: Jupyter Notebook, version-controlled ML pipelines

Core Knowledge & Competencies:

• Deep understanding of supervised learning, deep learning, and Transformer architectures for tabular data.
• Strong knowledge of adversarial machine learning threats, data poisoning attacks, and defense strategies.
• Practical expertise in reinforcement learning, including reward design and adaptive decision-making.
• Ability to conduct statistically sound evaluations, robustness testing, and sensitivity analysis.
• Strong analytical mindset, independent research capability, and meticulous documentation skills.
• Excellent written and verbal communication for collaboration with supervisors, reviewers, and research stakeholders.

Deliverables:

• Fully implemented and evaluated adversarial-robust fraud detection framework
• Reproducible source code, datasets, experiment logs, and documentation
• Scopus-ready research paper / dissertation with experimental validation 

Contact Info:

Phone: +91 95661 33822

Email: hr@workfoster.com

Click on Apply to know more.